Learning through ferroelectric domain dynamics in solid-state synapses

Sören Boyn (), Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi and Vincent Garcia ()
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Sören Boyn: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Julie Grollier: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Gwendal Lecerf: University of Bordeaux, IMS, UMR 5218
Bin Xu: University of Arkansas Fayetteville
Nicolas Locatelli: Centre de Nanosciences et de Nanotechnologies, CNRS, Univ. Paris Sud, Université Paris-Saclay
Stéphane Fusil: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Stéphanie Girod: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Cécile Carrétéro: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Karin Garcia: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Stéphane Xavier: Thales Research and Technology
Jean Tomas: University of Bordeaux, IMS, UMR 5218
Laurent Bellaiche: University of Arkansas Fayetteville
Manuel Bibes: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Agnès Barthélémy: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay
Sylvain Saïghi: University of Bordeaux, IMS, UMR 5218
Vincent Garcia: Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay

Nature Communications, 2017, vol. 8, issue 1, 1-7

Abstract: Abstract In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Date: 2017
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DOI: 10.1038/ncomms14736

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